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ISSN No: 2456

International Journal of Trend in Scientific

Research and

International Conference on Advanced Engineering

and Information Technology (ICAEIT

Study of Road

Approach with Link Based a

Tiopan H

Mulawarman University

ABSTRACT

Dynamical systems approach can describe the process of roads pavement damage and road maintenance funding allocation scenarios. One of the important thing to predict road maintenance fund needs in the future is how to estimate the traffic

through in each link on the road net work.

the reare two ways to fore cast future traffic flow, first approaches by link based and the other is network based. Network-based approach requires data flow speed of each link as an input. In

flow speed is affected by the

(International Roughness Index). This paper aims to look at the differences total requirement of road maintenance funds need for each year in which the estimated future traffic flows by link

network-based. There are four scenarios allocation of maintenance funds in each year of analysis, ie 20%, 40%. 60% and 80%of the total require ment. From the analysis, it is known that the total funding need fo road maintenance at the end of the estimated future traffic flows by way a network-based smaller when compared with the link based. In addition, it is known that the road maintenance fund allocation by 80% of the needs, it turns out the total funding nee maintenance at the end of the analysis is the

Keywords: Link Based, Network Based, Free Flow Speed.

1. INTRODUCTION

Road maintenance needs can be measured quantitatively by considering rate of service standards to be achieved, butt heal location of road

costs of tend not have measurable criteria.

system is very useful to understand the relationship

ISSN No: 2456 - 6470 | www.ijtsrd.com | Special Issue Publication

International Journal of Trend in Scientific

Research and Development (IJTSRD)

International Conference on Advanced Engineering

and Information Technology (ICAEIT-2017)

Road Maintenance Fund needs

with Link Based and Network Based

Tiopan H. M. Gultom, Tamrin, Fahrul Agus

Mulawarman University, Samarinda, East Borneo, Indonesia

Dynamical systems approach can describe the process of roads pavement damage and road maintenance funding allocation scenarios. One of the important thing to predict road maintenance fund needs in the that is going work. Currently cast future traffic flow, first approaches by link based and the other is

network-based approach requires data free dynamic, free valueo fIRI (International Roughness Index). This paper aims to look at the differences total requirement of road maintenance funds need for each year in which the imated future traffic flows by link-based and based. There are four scenarios allocation of maintenance funds in each year of analysis, ie 20%, ment. From the analysis, it is known that the total funding need for road maintenance at the end of the estimated future based smaller when compared with the link based. In addition, it is known that the road maintenance fund allocation by 80% of the needs, it turns out the total funding need maintenance at the end of the analysis is the smallest.

Link Based, Network Based, Free Flow

Road maintenance needs can be measured quantitatively by considering rate of service standards road maintenance criteria. Dynamic is very useful to understand the relationship

between qualitative and quantitative aspects of road asset management. Dynamical systems approach can also describe the process

scenarios of road maintenance funding allocation [1]. SaeidahFallah doing research whose goal is to see road maintenance funds needs

the her research did not look the relationship between implementation of road mai

amount of traffic.

One of the important thing

fund needs in the future is how to traffic in each link in the road network. Public Works Directorate of Highways generally use tools HDM (Highway and Managemen), this tool

predicting deterioration model and estimated road maintenance fund needs and

Both of these tools are

Development Bankdan World Bank Traffic estimation method used Link based approaches. In Figure

of traffic on a road network described follows.

Figure1. Schematic movement from C to B through

Special Issue Publication

International Conference on Advanced Engineering

Maintenance Fund needs

Based

between qualitative and quantitative aspects of road asset management. Dynamical systems approach can

of roads damage and scenarios of road maintenance funding allocation [1]. SaeidahFallah doing research whose goal is to see needs are dynamically, but not look the relationship between implementation of road maintenance delay to the

thing to predict maintenance is how to estimate the the road network. Ministry of of Highways Indonesia use tools HDM (Highway Development and Managemen), this tool is very helpful in model and estimated road and treatment scenarios. are introduced by Asian World Bank in early 2000. used in both this tool is

Figure 1, the movement network is generally as

(2)

Under normal conditions, traffic flow from C to Buses link 1-2, the total flow in sections 1-2 is V1-2. However, if in sections 1-2 de crease din per form an cedue to road deterioration and the road cannot be repaired immediately because of limited funds, so the speed in sections 1-2 become slower and then the travel time will increase. So flow in link 1-2 will reduce. Traffic flow that comes from C will choose a new route that can provide faster travel time, for example, using link 3-4 towards B. Consequently, the total traffic flow in link 3-4 will increase while in link 1-2 will reduce. Link-based method cannot describe this problem. It takes survey traffic counting to validation traffic flow that has been fore casted in previous years. Consider this problem, to fore cast future traffic flow becomes in accurate if multi plying the present traffic with growth factors in link. What if the traffic estimation method in link changed by using network-based approaches, where road users are assumed will be looking fastest travel time. This method requires information such as an Origin Destination Matrix (OD Matrix), zoning, road network map, the length of each road and width of the road. Needed OD matrix for each year of analysis, and the future of OD matrix resulted from base year OD matrix multiplied by Growth Factor, it is useful as a means for validation.

Both of these methods when used to estimate the road maintenance fund needs in the future will certainly be different, whether the link based method will give a total cost of road maintenance less than Network-Based approaches?.

1.1. RESEARCH PURPOSES

The purpose of this paper area:

A. Calculate traffic prediction in the link with link-based approaches, calculate the estimated cost of road maintenance every year and find out the value of IRI at the end of the analysis.

B. Calculate traffic in link that is dynamically change due to influenced by IRI with Network-based approaches, calculate the estimated cost of maintenance of roads each year and find out value of IRI at the end of the analysis

C. Shows the difference in total cost of road maintenance needs, with the allocation of

maintenance funds scripted20%, 40%,

60%and80% of the total annual cost of road maintenance. This scenario is applied to approaches Link based and Network based.

1.2. SCOPE OF STUDY

This study has several limitations that are used when analyzing, are:

A. Pavement type is a flexible pavement, and assumed the last maintenance done 2 years ago.

B. Growth Factor method of link-based and

Network- based method is 3%.

C. The IRI being used in the link is the average IRI D. During the evaluation there is no addition of roads

and increase capacity by adding lanes.

E. Drainse on the road network is considered in good condition

F. Traffic flow in the first year (2015) is the output of transport modeling with toolsEMME-4.

1.3. RESEARCH METHODOLOGY

In general, the research methodology is shown in Figure 2

Figure2. Research methodology

2.1. Road Net Work Data

The road network data used in this study are the road network map in shp format and data link characteristics (length, width and the value of IRI).

2.2. Estimates Traffic with Link Based Approaches

(3)

Where:

= number of vehicles per day on link I in year n = number of vehicles per day on link I in the

first year

x = growth factor

n = years

2.3. Estimates Traffic with Link Based Approaches

Forecasting traffic flow with network-based approaches using transport modeling tools EMME-4. It takes a information matrix Origin Destination (OD Matrix), growth factors OD Matrix, maps road network, road width, and the free flow speed on each road. Growth factors OD Matrix is3%. Tam in said that the transport in frastructure net work systems affect the movement of the system and vice versa [2]. Which differentiates it from link-based method compared to network-based is that the free flow speed is a function of IRI. While travel time is a function of free flow speed and travel time of the main things for road users to choose the sections that will be used. Roughness is unevenness of roads pavement surface, presented in a scale that describes unevenness of road surface. The International standard pavement roughness me asurement is called IRI, the unit is m / km. The worse the road conditions would lead to reduced travel speed son roads. Sayers. et, al [3]. Recommend ed value of the speed of some IRI values. Dwilak so no Toto doing research to see correlation bet ween free flow speed and IRI in Java [4].

The correlation equation addressed in equation 2:

WHERE:

Y = free flow speed (km/jam) X = average IRI in Link (m/km)

2.4 Road Deterioration Model

There are two types of models that can be used to predict Road deterioration (RD) and Work Effects (WE) [5]:

 Model Absoulut

 Model Incremental

Absolute models predict pavement conditions at a particular point in time as a function of the independent variable, while the incremental models give the changing conditions of the initial conditions

as a function of the independent variables. Both types of these models include the emperical models. Which means, these models are usually generated from the statistical analysis of the observations in the study of the trend of deterioration?

There are two types of de form at ion models dis tress, ie rutting and roughness. Rutting is defined as the accumulation of permanent deformation or not of overcoming traffic problem on the pavement, in the form of a tire tread groove within a certain time period [6]. There are four components of rutting, namely; initial densification, structural deformation, plastic deformation and wear form studded tires.

Roughness is defined as the deviation of the surface is completely flat with characteristics that affect the dynamic size vehicle, driving quality, load dynamics and surface drainage. Roughness model consists of several components, namely cracking, disintegration, deformation and maintenance.

In this study, the incremental roughness is calculated as a result of structural damage [7], the formula is:

WHERE:

SNPKb = Adjustment Structural Numbers due to cracking at the end of year analysis

SNPa = Adjustment Structural Numbers due to cracking in the early years analysis

dSNPK = reduction in adjusted structural number of pavement due to cracking  its value is 3.6

∆IRIs = incremental change in roughness due to structural deterioration during the analysis year (m/km, IRI)

YE4 = annual number of equivalent standard axles (millions/lane)

Kgm = calibration factor for environmental coefficient

 its value is 7

AGE3 = pavement age since last overlay (rehabilitation), reconstruction or new construction (tahun)

a0 = roughness coefficient structural

components 134

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WHERE:

IRIt = IRI at the end of year of analysis (m/km, IRI) IRI0 = IRI at early year of analysis (m/km, IRI)

2.5 Equivalent Standard Axle Loads (Esal)

This study has not valid ate the average weigh to feach class of vehicles passing through the road network of the study area. Therefore, using the Flexible Pavement Design Guidelines issued by the Ministry of Public Works Directorate General of Highways [8], where there are 8 classes of vehicles as shown Table1.

Tabel 1: Vehicle Damage Faktor (VDF) for each class group

No. Type of Class Value VDF

1 Passenger vehicles (Class2) 0.0001

2 Utility vehicles (Class 3&4) 0.0030

3 Small bus (Class5A) 0.3000

4 Big Bus(Class5B) 1.0000

5 small truck (Class6A) 0.8000

6 big truck (Class6B) 1.6000

7 Truck Trailers (Class 7A,7Bdan7C) 7.6000

2.6 Annual Axle Loading

Total weight of the axle for a year (ESAL) is calculated by multiplying the value of VDF with the number of vehicles passing each group on link.

WHERE:

ESALij= is the axle load during the i year in million ESAL for vehicle class j

VDFj = Vehicle damage factor for vehicle class j

AADTij= Annual average daily traffic class j during the I year

2.7 Treatment Program

Type of treatment is determined how much the damage, assessment parameter is the value of IRI.

Table 2 showed parameters of road maintenance treatment based on the value of IRI.

2.8 Unit Cost

Table 3 shown the unit price of each maintenance activity per line width, Unit price is obtained from the Directorate General of Highways for the price of 2015,

2.9 Scenario Allocated Road Maintenance Fund

Road maintenance fund allocation scenarios for analysis with Link-Based and Network-Based approach are 20%, 40%, 60% and 80% of total road maintenance funds per each year. Due to limited funding, the roads that need to be addressed in the coming year chosen by considering the parameter value IRI, Cost, and AADT. Each segment is scored against all three parameters. Priority is determined based on the total scores of all three parameters.

Calculation of road Deterioration, selection of treatment and priority, and funding requirement of road maintenance against budgeting allocation scenario is using micro soft office-excel as a tool

3. DATA AND ANALYSIS

Research area located in the province of Bali, this province was chosen because of its territory in the form of an island, the refore the traffic flow continuously from outside the region can be eliminated.

Implementation of regional autonomy to the district/city level, then published the Law of the Republic of Indonesia No.38of2004, which is on the Way. There are settings that road authority; nationals roads, province’s roads, county’s road and city’s road. Until now the technical information about the damage roads under authority of province, county’s and city’s is not as complete national’s road, which is the authority of central government. Therefore, this research is still limited to the national road.

3.1 National road network map and matrix origin Destin at ion Bali’s

This study uses a national road network map based on the Decree of the Minister of Public Works no. 248 / KPTS/M/2015.ThenationalroadnetworkmaponBali is land in shp format is shown in Figure3.

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The number of zones in the study area is divided into 33 zones. Zoning divided by district and sub-district administration. In downtown Denpasar and Bangli is divided into several zones based on the sub district. Origin and destination distribution in the base year (2011) is shown in Table 4.

3.2 Road characteristics and AADT at early year (2015)

Characteristic of data used is length of road, average width of the road, average IRI, and AADT. This data is obtained from Agency that handles national road son Bali is land. Every 6 months the agency traffic measurement and national road conditions conducted. Traffic information on national roads is used to validate the movement model from 2011to2015. This paper does not discuss in more detail how to validate models and forecasting models with the EMME4

transport modeling program. Table 5 shown

characteristic of national road on Bali is land.

4. Analysis

Analysis of road maintenance cost requirement with link based and network based approach for all national road net work in Bali is land. The analysis phase were traffic forecasting and then with variousfunding allocation scenarios calculated road maintenance fund need for each year.

4.1. Traffic forecasting with link-based approaches

Traffic forecast with Link-based approaches the way is multiply the growth factor to the existing traffic flow (seeeq.1). Soforthenext10years, the traffic fore casting are shown in Table 6. The traffic growth in each link is predicted 3% per year up to 2025.

4.2. Total road maintenance cost needs with link- based

From the results of Table 6, following the pattern of the calculation described in Figure 2 are used to determine the needs of road maintenance funds from 2015 to 2025 (detail see Table 7).

Linkbased_20 and so on, meaning was analyzed by means of link based approach and allocation of cost maintenance every year is 20% of total cost maintenance needs of road network every year analysis. Simulation with multiple scenarios allocation of funds, it is known that the total cost of road maintenance needs until 2025 with an allocation of 20% of the total cost of road maintenance needs per

year is Rp. 1.063.858.000.000. The condition of the road at the end of 2025 was deteriorating of the initial analysis (see Fig. 6). While if allocated funds amounting to 80% of the total requirement in each year, the total needs of the maintenance fund in 2015 until 2025 was Rp. 330.620.000.000,-. This number was not too significant losses than if allocated 60%of there quirement. This also applies to IRI, where allocations of 60% or 80% is not too large impact on improving the value of IRI.

Figure4. IRI Condition per year by different allocation budget scenario with link-based analysis

Allocation road maintenance fund 20% of the total cost needs of each year, then at the end of 2025 the total cost of road maintenance bigger, other impact is the average value of IRI in the road network is greater than if the allocation of road maintenance funds increased to 40% of the total cost needs of each year.

Figure 4 showed that there is a difference of each analysis at the end of 2025. This difference can be termed a san benefit for road managers. For road users, the advantage is travel speed undisrupted due to road damage, in the end there are savings in fuel consumption. The difference in total cost of road maintenance until 2025 in each year on the type of analysis is shown in Tabel 8. The differences can be a benefit for manager if allocation budget for maintenance bigger than it should be. Benefit from the allocation of 40% is the difference between the total cost of the allocation of 20% to the total cost of the allocation of 40%, thus permanently for all scenarios allocation.

Table 8: Total road maintenance cost needs with link- based and benefit

Type of Analysis

Total Cost

(2015-2025) Benefit

Linkbased_20 1.063.857,7 0

Linkbased_40 528.738,2 535.119,5

Linkbased_60 373.709,0 690.148,7

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4.3 Traffic forecast with network based

In this approaches, the traffic flow forecasted does not based on growth traffic in link, but Origin Destination Matrix growth every year. Then, Matrix Origin Destination (OD Matrix) is charged on road network. Modeling of traffic flow on the road network using EMME-4, production by INRO Canada.

One of the outputs of the EMME-4 used are traffic flow in every link in each year. The next step is to calculate the number of vehicles by vehicle class and then calculated the total ESAL (see Table 1 and equation6).

Damage incremental in each link as a result of total ESAL, incremental damage in each segment as a result of total ESAL, calculated using equation 2, then at the end of n year predicted value of IRI (see eq. 5) and the total funding of road maintenance e need sin yearn. Due to allocation of funds scripted always less than needed, then there is a process of evaluation of priority roads will be maintained, Once selected, anew IRI value used to calculate the free flow speed at the beginning of year n+ 1 using equation 2. This free flow speed information becomes the input current road network modeling with EMME-4 in the year n + 1, and so on.

As a result, there are differences in total traffic flow in link that is calculated by link-based approaches and Network-based. Table 9 shown the differences, The differences around 5%, traffic forecasting with link- based higher than network-based.

4.4. Total road maintenance cost needs with net work- based

Following calculation pattern described in Figure 2, the road maintenance cost needs from 2015 to 2025 showed at Table 10.

Network based_20 and so on, meaning was analyzed by means of network based approach and allocation of cost maintenance every year is 20% of total cost maintenance needs of road network every year analysis. Simulation with multiple scenarios allocation of funds, it is known that the total cost of road maintenance needs until 2025 with an allocation of 20% of the total cost of road maintenance needs per year is Rp. 781 .422. 000. 000.

Figure5. IRI Condition per year by Different allocation budget scenario

With network-based analysis

Similar to the results of analysis with Link based approaches, Using Network-based approaches and the planned allocation of road maintenance fund 20% of the total requirement per year, the total cost of maintenance of the road at the end of 2025 is the biggest than if the funds allocated is greater than 20%. It also gives the average value of IRI in the road network was getting worse. In Figure 5 is shown that the difference IRI condition in difference allocation scenarios maintenance of roads. Average of IRI_20 and so on, meaning average IRI per year for allocation budget 20%from maintenance needs, The difference in total cost of road maintenance until 2025 in each year on the type of analysis is shown in Table11..

Table 11: Total road maintenance cost needs with network-based and benefit

Analysis Types Total Cost (Rp. 1.000.000,-)

Benefit (Rp.)

Networkbased_20 781,422 0

Networkbased_40 521,417 260,005

Networkbased_60 432,214 349,208

Networkbased_80 324,832 456,590

5. DISCUSSION

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implementation of these survey, it will add to the cost for road asset manager. These funds would be much better to increase road maintenance fund.

While network-based method that is modified to consider the influence of the IRI, it takes more effort, because managers need additional knowledge that is road transport modeling. But is more indicative the actual condition road user behavior.

Table 9 and table 10 indicated differences in estimation road maintenance costs needs in the future, which is analyzed with link-based and network-based approaches. With link-based approaches, road maintenance fund allocation plan by 20% of the total requirement road maintenance fund in 2015, the total requirement for 10 year analysis (2015-2025) was Rp. 1.063.857.700.000, this figure is very much compared to when traffic is expected in the future using a network- based approaches, as well as considering the IRI against the free flow speed in the next year, then total needs of road maintenance fund is Rp.781.422.000.000.

Table 12: The diferences Total Cost Maintenance Link based and Network based

Alloc. Plan.

Total Maintenance Cost (Fiscal Need) at The End

2025 (Rp. 1.000.000,-)

Difference Analysis Types

Link-based Network- based

20% 1,063,857.71 781,422 282,436.03

40% 528,738.24 521,417 7,321.48

60% 373,709.04 432,214 (58,505.07)

80% 330,619.55 324,832 5,788.04

Allocations plan 60% of the total road maintenance cost needs with link-based less than network-based. There are different sections that need to be maintained, due to differences traffic flow forecast. Consequently, there is differences roads maintenance programme. Example, randomly drawn average daily daily traffic (AADT) estimates using link based and network-based methods on 4 road segments in 2018, 2022 and 2025. Table 13 is shown that AADT link-based increases constantly according to the growth assumption on the segment, while network-based can sometimes be higher than linked AADT estimates but in the coming year may be lower.

Table 13: AADT using network-based and link-based

Years Links AADT by Networkbased AADT by Link based

2018

Sp.Cokroaminoto -Sp.Tohpati 18.479 17.607

Jln. A. Yani - Jln. S. Parman (Seririt) 340 329

Bts. Kota Gianyar -Sidan 2.672 2.549

Sp. Lap. Terbang (Dps) – TuguNgurah Ra 35.976 34.273

Jln. AstinaTimur(Gianyar) 2.672 2.549

2022

Sp.Cokroaminoto - Sp.Tohpati (Jln. G.Su 21.194 21.023

Jln. A. Yani - Jln. S.Parman (Seririt) 404 392

Bts. Kota Gianyar -Sidan 2.929 3.044

Sp. Lap. Terbang (Dps) - TuguNgurahRa 39.301 40.924

Jln. AstinaTimur (Gianyar) 2.790 3.044

This difference can have an impact on the type of program on the road. See Table14, using network-based methods, road number 33 are estimated to require preventive maintenance at year of 2022. While in the link base method, preventive maintenance is predicted need at road number 27, while road number 33 required routine maintenance.

6. CONCLUSION

From the analysis, and network-based approach to link-based, it can be concluded some of the following: 1. Traffic Flow fore casting approaches link-based

will always be increased even though the road is not repaired, it becomes different if carried out with a network-based approach.

2. Allocating 20% of the total road maintenance needs, at the end of2025 if calculated by link based analysis will have the total cost 36% higher than network based analysis.

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4. At the end of 2025, the total cost of maintenance smaller when analyzed with a network-based approach, difference could reach 28% of the link- based analysis.

7. ACKNOWLEDGEMENTS

The researchers would like to thank to rector of Mulawarman University and scholar for his support.

8. REFERENCES

1. Saedeh Fallah-Fini, “Optimizing highway

maintenance operations”, Dynamic

considerations. System dynamic review, Vol. 26, No. 3. pp 216 – 238,2010

2. Tamin O.Z, Planning and transport modeling: Penerbit ITB. Edisi 2. pp. 28,2000.

3. Sayers, “Guidelines for Conducting and

Calibrating Road Roughness Measurements”, World Bank technical paper number 46. pp. 72, 1986.

4. Dwilaksono Toto, “Pe model an Peren Canaan Komprihensip Pembangunan In frast rukturJalan (TinjauanJaringanJalan)”,ThesisUI.pp.64,2002

5. Odoki (version 2). Manual Highway Development

and Management 4. Vol. 4. pp. C1-8.

6. Odoki (version 2). Manual Highway Development

and Management 4. Vol. 4. pp.C2-7

7. Odoki (version 2). Manual Highway Development

and Management 4. Vol. 4. pp.C2-55

8. Ministry of Public Works Directorate General of Highways, Pavement design manual. pp. 19,2013

9. Badawi,“Studianalisispemelihanskenariopengemb

anganangkutanumumkawasankota Denpasar

danKabupatenBadungmenggunakanperangkatluna k EMME-4”, ITB, 2013.

10.Ministry of Public Works Directorate General of Highways. (2014).

Table 2: Road maintenance programme consider IRI [10]

Name of

program Name of Sub- Programme Range of IRI IRI(n+1) Treatment Details

Routine maintenance

Routine Maintenance(Pr) 0– 3.0 IRI min +0.5 Maintenance of drainage systems

Conditions Routine

Maintenance (Prk) 3.0–4 IRI min - 0.5

maintenance of road shoulders; vegetation clerance Compaction, leveling, and

reformation of shoulder. Preventif maintenance

(Pp) 4.0–6.0 IRI min - 0.5 Patching, sealing for surface crack, road maintenance equipment Minor rehabilitation

(RMn) 6.0–8.0 to3.0 Non-structural overlay

Improvement Major rehabilitation (Rmy) 8.0–12.0 to 3.0 Structural overlay and repair drainage system

Table 3: Unit cost for each treatment

Nu. Description Unit Cost/km(Rp.1000) Routine maintenance and Conditions (IRI 0 - 4)

Pav. width upto 4.5 m and shoulder 2x1m Km 36,785

I Pav. width upto 5 m and shoulder 2x1m Km 37,488

Pav. width upto 6 m and shoulder 2x1.5m Km 40,866

Pav. width upto 7 m and shoulder 2x2m Km 44,244

Pav. width upto s/d 14 m and shoulder 2x2m Km 54,987

Preventif maintenance (IRI 4 - 6)

Pav. width upto 4.5 m and shoulder 2x1 m Km 468,413

II Pav. width upto 5 m and shoulder 2x1 m Km 510,385

Pav. width upto 6 m and shoulder 2x1.5 m Km 607,365

Pav. width upto 7 m and shoulder 2x2 m Km 694,582

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Minor Rehabilitation (IRI 6.0 - 8.0)

Pav. width upto 4.5 m and shoulder 2x1 m Km 780,689

Pav. width upto 5 m and shoulder 2x1 m Km 850,641

III Pav. width upto 6 m and shoulder 2x1.5 m Km 1,012,275

Pav. width upto 7 m and shoulder 2x2 m Km 1,157,637

Pav. width upto 14 m and shoulder 2x2 m Km 2,274,165

Improvement

Major Rehabilitation (IRI 8.0 - 12.0)

Pav. width upto 4.5 m and shoulder 2x1 m Km 2,431,018

Pav. width upto 5 m and shoulder 2x1 m Km 2,675,174

Pav. width upto 6 m and shoulder 2x1.5 m Km 3,220,695

Pav. width upto 7 m and shoulder 2x2 m Km 3,757,813

Pav. width upto 14 m and shoulder 2x2 m Km 7,310,172

IV Reconstruction (IRI > 12)

Pav. width upto 4.5 m and shoulder 2x1 m Km 3,006,939

Pav. width upto 5 m and shoulder 2x1 m Km 3,314,358

Pav. width upto 6 m and shoulder 2x1.5 m Km 4,279,337

Pav. width upto 7 m and shoulder 2x2 m Km 4,993,006

Pav. width upto 14 m and shoulder 2x2 m Km 9,774,596

Table4. Original and destination matrix on base year 2011[9]

N o .

Zona

Zona

1 2 3 4 5 6 7 8 9 10 11 12 3 1 14 15 16 17 18 19 0 2 21 22 23 24 25 26 27 28 29 30 31 32 33

1 Pecatu 0 50 11

0 2 0 5 0 1

0 0 0 0

1

0 0

7

0 0

4 0 1 0 3 0 3 0 3 0 3 0 1

0 0 0

4 0 1 0 6 0 5 0 2 9 0 3 0 4

0 0 0 0

3 0 2 Tanju ngBen oa 3

0 0

1 0 0 4 0 1 3 0 1

0 0 0 20 10 0

1 8 0

3

0 60 0 10

1 3 0 8 0 1 5 0 1

0 10 0 90 10 50 1 8 0

2 5

0 0

5

0 10 0 0 50

3 Jim baran 1 3 0

3 0

0 0

4 0 2 7 0 1

0 0

1 0

1 0

5

0 0

2 6 0 2 0 7 0 1 0 6 0 1 0 0 1 7 0 2 6 0 4 0 3

0 0

5 0 3 0 1 5 0 2 8 0 2 0 0 3 0 7 0 1 0 3

0 0

1 0

4 Tuban 30

1 5 0

7

0 0

7

0 0 0 0 0 0 0

1 3 0

0 90 20 80 1 7 0 5 0 1 1 0 2 0 1

0 0

2

0 0

4 0 1 6 0 1 1 0 2

0 0 0 0 0 0

5 Kuta

3 0 0 5 8 0 3 9 0 3

0 0

1

0 0 0 0 0 0

3 7 0

0 30 0 40 80 10

1 7 0 7 0 2

0 0

1 0 3 0 1 7 0 1 5 0 1 3 0 1 0 7 0 1

0 0 0

4 0

6 Kerobokan 30 50 80 10 50 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 Kerob okan

Kelod 0 0

1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 Canggu 30 10 10 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 20 0 0 0 0 0 0

9 Dalung

1 2 0 1 1 0 7 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 Meng wi 1 2 0 1 8 0 9 0 8 0 4

0 0 0 0

1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 Abian sem al 2 0 2 0 2

0 0

2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 2 Dangi npuri 1 8 0 5 5 0 6 7 0 3 9 0 8 8

0 0

1

0 0 0 0 0 0

1 0

1

0 0

2 0 4 0 2 0 1 6 0 1

0 0

1

0 0 0 0

1 0 1 0 1 0 1

0 0

1 0

1

0 0

1 3

Sum

erta 0

2 0 3 0 1 0 3

0 0 0

1

0 0 0 0 0 0 0 0 0

2

0 0

1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 4 Kesim an 1 0 0 6 0 4

0 0

2

0 0 0 0 0 0 0 0 0 0 0 0 0 0

1

0 0 0 0 0 0

1

0 0 0 0 0 0 0 0 0

(10)

5 h 0 0 0 0 1 6 Pedun gan 8 0 9 0 9 0 1 0 3

0 0 0 0 0 0 0

7

0 0

1 0

1

0 0

3 0 5 0 1 4 0

0 0 0 30 0 10 10 0 0 20 10 0 0 0

1 7 Seseta n 9 0 1 9 0 1 9 0 3 0 8

0 0

1

0 0 0 0 0

3

0 0 0 0

7

0 0

4 0

9

0 0

1

0 0

3 0 1 0 1 0 4 0 4 0 1

0 0 0 0

1 0 1 0 1 8 Sidaka rya 9 0 1 3 0 2 1 0 1 0 0 7

0 0 0 0 0 0

1

0 0 0 0 0

5 0

7

0 0

8 0

2

0 0 0

1

0 0

2 0

1 0

4

0 0 0

2

0 0 0 0

1

9 Sanur

1 8 0 2 3 0 2 8 0 1 6 0 2 8 0

0 0 0 0 0 0 20 0 0 0 10 50 40 0 0 0 0 10 0 10 20 10 0 0 0 0 0 0

2

0 Pemongan 60 1 2 0

9

0 30 30 0 0 0 0 0 0 10 0 10 10 0 30 30

1 2

0 0 0

1

0 0 0 0 10 50 10 0 0 0 0 20

2

1 Ubung

4 0 7 0 7 0 1 0 6

0 0 0 0 0 0 0 0 0 0 0 0 0 0

1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 2 Peguy angan 1 0 6 0 3

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2

3 Tonja 40 60

1 1 0

0 40 0 0 0 0 0 0 0 0 0 0 0 20 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 4 Padan gsamb ian 1 0 0 4 0 9 0 4 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1

0 0 0 0

1 0

1 0

2

0 0 0 0 0

2 5 Tegal harum 1 2 0 3 4 0 3 3 0 9 0 2 9 0

0 0 0 0 0 0 20 0 0 0 0 20 10 40 0 1 0 0 10 0 0 0 20 0 0 0 0 0 10

2 6 Dauhp uri 9 0 2 3 0 4 2 0 1 2 0 2 6 0

0 0 0 0 0 0 0 0 0 0 0 30 10 40 0 0 0 0 0 10 0 0 0 10 0 0 0 0

2 7 Giany ar 1 9 0 1 1 0 1 7 0 1 0 6

0 0 0 0 0 0 0 0 0 0 0 0

1

0 0

1

0 0 0 0 0 0 0 0 0

9 0 5 4 8 9 4 8 1 1 7 8 2 8 Klung kung 1 0 1 0 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8

0 0

2 9

3 3

1

9 4

2 2 2

9 Karangasem 20 40 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 48 29 0 33 42 8 29

3

0 Bangli

3 0

2 0

1

0 0 0 0 0 0 0 0 0 0 0 0 0

1

0 0 0 0 0 0 0 0 0 0 0

8 0

3 4

3

4 0

3

0 6

3 3 3

1 Buleleng 0 40 20 10 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 44 20 43 30 0 23 51

3 2

Jem

brana 0

5 0

5

0 0 0 0 0 0 0 0 0

1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1

1 4 8 6

2

3 0

1 5 3 3 Taban an 1 5 0 8 0 7 0 6 0 7

0 0 0 0 0 0 0

2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 1 2 3 3 0 3 3 5 1 1

5 0

Table 5: Road characteristics and AADT at early year [10]

Seg.

Nu. Road segmen names

Length (Km) Average Width (M) SDI Average IRI Average AAD T

001 Gilimanuk – Cekik 3,041 10,928 2,42 3,307 5141

002 Cekik - Bts. Kota Negara 27,224 7,139 13,28 3,354 22319

Jln. A. Yani - Jln. Udayana (Negara) 1,923 12,181 0,75 3,325 11819

003 Bts. Kota Negara – Pekutatan 20,445 7,330 0,32 3,285 6880

Jln. Sudirman, Gajahmada (Negara) 4,466 9,181 2,22 3,337 13001

004 Pekutatan – Antosari 29,964 7,198 6,81 3,491 15807

005 Antosari - Bts. Kota Tabanan 17,262 8,086 12,11 3,621 22689

Simp. Kediri – Pesiapan (Tabanan) 4,020 17,776 2,56 3,628 32028

006 Bts. Kota Tabanan – Mengwitani 1,462 13,000 1,00 3,240 50795

Jln. A. Yani (Tabanan) 2,025 11,900 0,71 3,785 43812

007 Mengwitani - Bts. Kota Denpasar 7,385 14,534 10,88 3,018 64924

Jln. Cokroaminoto (Dps) 3,826 11,132 25,64 2,870 33975

Jln. Cokroaminoto (Dps) 0,979 11,000 19,50 3,263 53609

Jln. Sutomo (Dps) 0,936 12,500 0,00 2,500 28094

(11)

Jln. Wahidin (Dps) 0,232 8,000 0,00 4,133 30654

Jln. Thamrin (Dps) 0,376 9,000 11,25 3,875 21917

008 Sp.Cokroaminoto - Sp.Kerobokan 3,788 14,000 0,92 3,661 47775

009 Jln. GunungAgung - AksesKargo 4,424 13,435 2,33 3,319 21474

010 Jln. Western Ring Road (Sp.GatotSubroto 4,460 14,000 1,00 3,000 21474

011 Kuta - Banjar Taman 5,467 14,000 8,00 3,379 21474

012 Denpasar – Tuban 10,781 8,677 0,90 3,272 23140

013 Simp. Kuta - TuguNgurah Rai 2,726 16,289 1,61 3,657 26367

014 Sp. Lap. Terbang (Dps) - TuguNgurah Ra 0,350 18,000 0,00 3,050 20037

015 TuguNgurah Rai - Nusa Dua 9,536 13,700 8,20 2,602 47469

016 SimpangKuta - Simp. Pesanggaran 3,693 13,000 23,38 3,419 38948

017 Simp.Pesanggaran - GerbangBenoa 0,604 19,000 2,14 4,067 7887

018 SimpangPesanggaran - Simpang Sanur 8,390 13,824 4,61 3,434 23452

019 Simpang Sanur - SimpangTohpati 4,390 13,023 8,86 2,805 24974

020 Sp.Cokroaminoto - Sp.Tohpati (Jln. G. Su 5,357 13,198 0,91 3,194 24712

021 Sp. PantaiSiut – Kosamba 11,806 7,000 0,74 3,011 55683

Sp. Tohpati - Sp. PantaiSiut 15,899 16,000 2,34 2,892 55683

022 Sp. Tohpati – Sakah 12,965 11,452 0,85 3,740 55683

023 Sakah - Blahbatu 3,027 8,111 0,48 3,603 26302

024 Blahbatu – Semebaung 3,765 8,433 0,00 3,162 28347

025 Semebaung - Bts. Kota Gianyar 2,095 8,050 0,00 3,086 31158

Jln. CiungWanara (Gianyar) 0,537 14,000 0,00 2,950 31158

Jln. Astina Utara (Gianyar) 0,398 10,000 0,00 4,300 31158

026 Bts. Kota Gianyar – Sidan 1,253 12,000 0,00 3,131 31965

Jln. Ngurah Rai (Gianyar) 0,667 7,000 2,14 3,257 31965

Jln. AstinaTimur (Gianyar) 0,984 8,228 0,00 4,056 31965

027 Sidan - Bts. Kota Klungkung 7,180 7,500 7,43 3,244 13311

Jln. UntungSuropati, Flamboyan

(Semarap 1,769 8,335 2,78 3,254 13311

028 Bts. Kota Klungkung - Kosamba (Bts.

Kab. 10,101 11,300 1,62 3,476 31697

Jln. Diponegoro (Semarapura) 0,815 7,251 0,00 3,247 31697

029 Kosamba (Bts. Kab. Karangasem) –Angente 4,376 8,949 5,80 3,701 11587

030 Angentelu – Padangbai 2,048 7,324 1,59 3,350 951

031 Cekik – Seririt 62,910 8,600 1,96 3,313 1578

Jln. A. Yani - Jln. S. Parman (Seririt) 0,741 7,892 0,00 3,440 1160

032 Seririt - Bts. Kota Singaraja 18,656 16,224 7,85 3,453 15596

Jln. Gajahmada - Dr. Sutomo - A. Yani (S 4,090 7,708 0,00 3,675 10857

033 Bts. Kota Singaraja - Kubutambahan 6,199 10,374 1,53 3,720 17764

Jln. Ng. Rai Selatan - Jln. Pramuka - Jl 6,007 7,004 1,56 3,102 13031

034 Kubutambahan-Km

124Dps(BonDalem/Ds. 46,000 7,000 0,58 3,761 3750

035 Km 124 Dps (Bon Dalem/Ds.

Tembok)-Bts. 30,637 9,027 0,90 3,327 8735

Jln. UntungSurapati (Amlapura) 2,825 6,656 16,38 3,031 4068

036 Bts. Kota Amlapura – Angentelu 20,331 7,431 6,02 3,671 16208

Jln. Sudirman - A. Yani (Amlapura) 2,584 9,871 1,11 3,271 16208

037 Bts. Kota Singaraja – Mengwitani 60,425 7,500 9,39 3,722 2564

Jln. JelantikGingsir - Veteran (Singara 3,425 10,000 4,29 3,745 632

(12)

Table 6: Traffic forecasting by link-based approach

N

u. Name so Link

VOLUME

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

1 Gilimanuk – Cekik 319 329 338 349 359 370 381 392 404 416 429

2 Cekik - Bts. Kota Negara 319 329 338 349 359 370 381 392 404 416 429

3 Jln. A. Yani -Jln. Udayana(Negara) 319 329 338 349 359 370 381 392 404 416 429

4 Bts. KotaNegara-PekutatanGajahmada 2134 2198 2264 2332 2402 2474 2548 2625 2703 2784 2868

5 Jln. Sudirman, (Negara) 2134 2198 2264 2332 2402 2474 2548 2625 2703 2784 2868

6 Pekutatan-Antosari 2134 2198 2264 2332 2402 2474 2548 2625 2703 2784 2868

7 Antosari - Bts. Kota Tabanan 2134 2198 2264 2332 2402 2474 2548 2625 2703 2784 2868

8 Simp. Kediri - Pesiapan (Tabanan) 2134 2198 2264 2332 2402 2474 2548 2625 2703 2784 2868

9 Bts. KotaTabanan-Mengwitani 11066 11398 11740 12092 12455 12829 13213 13610 14018 14439 14872

10 Jln. A. Yani (Tabanan) 11066 11398 11740 12092 12455 12829 13213 13610 14018 14439 14872

11 Mengwitani -Bts.Kota Denpasar 20834 21459 22103 22766 23449 24152 24877 25623 26392 27184 27999

12 Jln. Cokroaminoto(Dps) 20834 21459 22103 22766 23449 24152 24877 25623 26392 27184 27999

13 Jln. Cokroaminoto(Dps) 20834 21459 22103 22766 23449 24152 24877 25623 26392 27184 27999

14 Jln.Sutomo(Dps) 20834 21459 22103 22766 23449 24152 24877 25623 26392 27184 27999

15 Jln. Setiabudi (Dps) 20834 21459 22103 22766 23449 24152 24877 25623 26392 27184 27999

16 Jln. Wahidin (Dps) 20834 21459 22103 22766 23449 24152 24877 25623 26392 27184 27999

17 Jln. Thamrin (Dps) 20834 21459 22103 22766 23449 24152 24877 25623 26392 27184 27999

18 Sp.Cokroaminoto-

Sp.Kerobokan 17963 18502 19057 19629 20218 20824 21449 22092 22755 23438 24141

19 Jln. GunungAgung-

AksesKargo 25542 26308 27098 27910 28748 29610 30498 31413 32356 33327 34326

20 Jln. Western Ring Road

(Sp. Gato Subro to 25487 26252 27039 27850 28686 29546 30433 31346 32286 33255 34252

21 Kuta –BanjarTaman 33572 34579 35617 36685 37786 38919 40087 41289 42528 43804 45118

22 Denpasar- Tuban 59895 61692 63543 65449 67412 69435 71518 73663 75873 78149 80494

23 Simp. Kuta – Tugu NgurahRai 73854 76070 78352 80702 83123 85617 88186 90831 93556 96363 99254

24 Sp. Lap. Terbang(Dps)-

TuguNgurah Ra 33275 34273 35301 36360 37451 38575 39732 40924 42152 43416 44719

25 TuguNgurah Rai-Nusa

Dua 55825 57500 59225 61001 62832 64716 66658 68658 70717 72839 75024

26 SimpangKuta-Simp.

Pesanggaran 71137 73271 75469 77733 80065 82467 84941 87490 90114 92818 95602

27 Simp.Pesanggaran-

GerbangBenoa 9581 9868 10164 10469 10783 11107 11440 11783 12137 12501 12876

28 SimpangPesanggaran-

Simpang Sanur 29095 29968 30867 31793 32747 33729 34741 35783 36857 37962 39101

29 Simpang Sanur-Simpang

Tohpati 15334 15794 16268 16756 17259 17776 18310 18859 19425 20007 20608

30 Sp.Cokroaminoto-

Sp.Tohpati (Jln. G. Su 17094 17607 18135 18679 19239 19817 20411 21023 21654 22304 22973

31 Sp. PantaiSiut-Kosamba 7018 7229 7445 7669 7899 8136 8380 8631 8890 9157 9432

32 Sp.Tohpati-Sp.PantaiSiut 7018 7229 7445 7669 7899 8136 8380 8631 8890 9157 9432

33 Sp. Tohpati-Sakah 20482 21096 21729 22381 23053 23744 24457 25190 25946 26724 27526

34 Sakah-Blahbatu 19514 20099 20702 21323 21963 22622 23301 24000 24720 25461 26225

35 Blahbatu-Semebaung 19514 20099 20702 21323 21963 22622 23301 24000 24720 25461 26225

36 Semebaung - Bts. Kota Gianyar 19514 20099 20702 21323 21963 22622 23301 24000 24720 25461 26225

(13)

(Gianyar)

38 Jln.AstinaUtara(Gianyar) 19514 20099 20702 21323 21963 22622 23301 24000 24720 25461 26225

39 Bts. Kota Gianyar-Sidan 2475 2549 2626 2704 2786 2869 2955 3044 3135 3229 3326

40 Jln. NgurahRai(Gianyar) 2475 2549 2626 2704 2786 2869 2955 3044 3135 3229 3326

41 Jln.AstinaTimur (Gianyar) 2475 2549 2626 2704 2786 2869 2955 3044 3135 3229 3326

42 Sidan -Bts. Kota

Klungkung 2475 2549 2626 2704 2786 2869 2955 3044 3135 3229 3326

43 Jln.UntungSuropati,

Flamboyan (Semarap 2475 2549 2626 2704 2786 2869 2955 3044 3135 3229 3326

44 Bts. Kota Klungkung-Kosamba (Bts.Kab. 3531 3637 3746 3858 3974 4093 4216 4343 4473 4607 4745

45 Jln. Diponegoro (Semarapura) 3531 3637 3746 3858 3974 4093 4216 4343 4473 4607 4745

46 Kosamba(Bts.Kab.Karangasem) – Angente 5709 5880 6057 6238 6426 6618 6817 7021 7232 7449 7672

47 Angentelu- Padangbai 5709 5880 6057 6238 6426 6618 6817 7021 7232 7449 7672

48 Cekik–Seririt 319 329 338 349 359 370 381 392 404 416 429

49 Jln. A. Yani -Jln.S.Parman (Seririt) 319 329 338 349 359 370 381 392 404 416 429

50 Seririt-Bts.KotaSingaraja 319 329 338 349 359 370 381 392 404 416 429

51 Jln. Gajahmada- Dr.Sutomo – A.Yani(s 319 329 338 349 359 370 381 392 404 416 429

52 Bts. KotaSingaraja – Kubutambahan Pram 1001 1031 1062 1094 1127 1160 1195 1231 1268 1306 1345

53 Jln. Ng. Rai Selatan-Jln.uka-J1 1001 1031 1062 1094 1127 1160 1195 1231 1268 1306 1345

54 Kubutambahan - Km 124 Dps (Bon Dalem/Ds. 1001 1031 1062 1094 1127 1160 1195 1231 1268 1306 1345

55 Km124Dps (Bon Dalem/ Ds. Tembok)- Bts. 594 612 630 649 669 689 709 731 752 775 798

56 Jln. UntungSurapati (Amlapura) 594 612 630 649 669 689 709 731 752 775 798

57 Bts. KotaAmlapura–Angentelu 5709 5880 6057 6238 6426 6618 6817 7021 7232 7449 7672

58 Jln. Sudirman - A. Yani (Amlapura) 5709 5880 6057 6238 6426 6618 6817 7021 7232 7449 7672

59 Bts. KotaSingaraja–Mengwitani 2981 3070 3163 3257 3355 3456 3559 3666 3776 3890 4006

60 Jln. JelantikGingsir-Veteran (Singara 2981 3070 3163 3257 3355 3456 3559 3666 3776 3890 4006

61 Sp. 3 Mengwi-Beringkit 10472 10786 11110 11443 11786 12140 12504 12879 13266 13664 14073

Table 7: Total road maintenance cost needs every year with link-based

Total maintenance cost needs every years(Rp.1.000.000,-) Total cost

Analysis Types

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Linkbased _20

33,2 26

32,6 56

32,6 56

57,2 63

74,0 33

85,1 20

72,2 85

106,0 90

119,5 70

225,7 54

225,2 04

1,063,8 58 Linkbased

_40

33,2 26

32,6 56

31,3 68

43,4 87

46,1 39

46,1 39

47,4 21

47,42 1

53,67 3

68,17 9

79,03 0

528,73 8 Linkbased

_60

33,2 26

31,6 48

31,6 48

32,6 56

32,6 56

32,6 56

33,9 38

32,65 0

32,65 0

37,48 6

42,49 6

373,70 9 Linkbased

(14)

Table 9: The differences in link traffic flow (link-based vs network-based)

No Length(m) IRI2015 IRI2020

LinkBased_20 approach NetworkBased_20 approach AADT 2015 AADT 2020 AADT 2015 AADT 2020

15 Jln. Setiabudi

(Dps) 0.77 4.21 4.74 20,834 24,152 20,834 22,979

16 Jln. Wahidin (Dps) 0.23 4.23 4.76 20,834 24,152 20,834 22,979

17 Jln. Thamrin (Dps) 0.38 3.97 4.50 20,834 24,152 20,834 22,978

Table 10: The total road maintenance cost per year with network-based

Analysis Types

Total Maintenance Cost (Fiscal Need) per Years (Rp. 1.000.000,-) Total cost(Rp.) 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Networkb

ased_20 33,226 32,656 32,656 57,263 59,915 66,167 66,167 90,267 93,909 131,420 117,777 781,422 Networkb

ased_40

33,2 26

32,6 56

31,3 68

38,5 27

41,1 78

41,1 78

41,1 78

59,4 38

59,4 38

69,1 09

74,1

19 521,417

Networkb ased_60

33,2 26

31,6 48

30,3 59

37,7 36

30,5 77

37,7 36

30,5 77

48,8 37

48,8 37

48,8 37

53,8

47 432,214

Networkb ased_80

33,2 26

32,6 56

28,7 72

28,7 72

28,7 72

28,7 72

28,7 72

28,7 72

28,7 72

28,7 72

28,7

72 324,832

Table 14: Differences maintenance program due to differences in forecasts traffic flow

Ye ar

Networkbased_60 Linkbased_60 Nu

. Link Lenght(Km) Width(M) Treatment Cost (Rp.) Nu. Link Lenght (Km) Width(m) Treat of Cost (Rp)

20 22

33 Sp. Tohpati - Sakah

12,97 11,5 Preve

ntif

17.69 0.730 27

Simp. Pesanggara n Gerbang Benoa

0,60 19,0 Prev

entif 824. 157

13 Jln. Cokroa minoto (Dps)

0,98 11,0 Preventif 1.335.845 15 Jln. Setiabudi (Dps)

0,77 10,0 Preventif 1.05 0.66 4

15 Jln. Setiabu di(Dps)

0,77 10,0 Preventif 1.050.664 16 Jln. Wahidin (Dps)

0,23 8,0 Preventif 316.564

16 Jln. Wahidi n (Dps)

0,23 8,0 Preventif 316.564 17 Jln. Thamrin (Dps)

0,38 9,0 Preventif 513.052

17 Jln. Thamri

n (Dps) 0,38 9,0

Preve ntif

513.0

52 13

Jln.

Cokroamin

o to (Dps) 0,98 11,0

Prev entif

Figure

Table 3 shown the unit price of each maintenance activity per line width, Unit price is obtained from the Directorate General of Highways for the price of 2015,
Table 13: AADT using network-based and link-based
Table 2: Road maintenance programme consider IRI [10]
Table 6: Traffic forecasting by link-based approach
+3

References

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